A hybrid approach to multimodal biometric recognition based on feature-level fusion of face, two irises, and both thumbprints

Mohammad H Safavipour, Mohammad A Doostari, Hamed Sadjedi

DOI: 10.4103/jmss.jmss_103_21

Abstract


Background:The most significant motivations for designing multi-biometric systems are high-accuracy recognition, high-security assurances as well as overcoming the limitations like non-universality, noisy sensor data, and large intra-user variations. Therefore, choosing data for fusion is of high significance for the design of a multimodal biometric system. The feature vectors contain richer information than the scores, decisions and even raw data, thereby making feature-level fusion more effective than other levels. Method: In the proposed method, kernel is used for fusion in feature space. First, the face features are extracted using kernel-based methods, the features of both right and left irises are extracted using Hough Transform and Daugman algorithm methods, and the features of both thumb prints are extracted using the Gabor filter bank. Second, after normalization operations, we use kernel methods to map the feature vectors to a kernel Hilbert space where non-linear relations are shown as linear for the purpose of compatibility of feature spaces. Then, dimensionality reduction algorithms are used to the fusion of the feature vectors extracted from fingerprints, irises and the face. since the proposed system uses face, both right 7and left irises and right and left thumbprints, it is hybrid multi-biometric system. We c8arried out the tests on seven databases. Results: Our results show that the hybrid multimodal template, while being secure against spoof attacks and making the system robust, can use the dimensionality of only 15 features to increase the accuracy of a hybrid multimodal biometric system to 100%, which shows a significant improvement compared with uni-biometric and other multimodal systems. Conclusion: The proposed method can be used to search large databases. Consequently, a large database of a secure multimodal template could be correctly differentiated based on the corresponding class of a test sample without any consistency error.

Keywords


Feature-level fusion, hybrid, kernel, multimodal biometric

Full Text:

PDF

References


Li Y, Zou B, Deng SH, Zhou G. Using feature fusion strategies in continuous authentication on smartphones. IEEE Internet Comput 2020;24:49-56.

Joseph T, Kalaiselvan SA, Aswathy SU, Radhakrishnan R, Shamna AR. A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment. J Ambient Intell Human Comput 2021;12:6141-9.

Zhifang W, Jiaqi1 Z, Yanchao L, Guoqiang L, Qi H. Multi-feature Multimodal Biometric Recognition Based on Quaternion Locality Preserving Projection. Chin J Electron 2019;28:789-96.

Tong Y, Bai J, Chen X. Research on multi-sensor data fusion technology. J Phys Conf Ser 2020;1624:032046.

Zhang Y, Gao C, Pan S, Li Z, Xu Y, Qiu H. A score-level fusion of fingerprint matching with fingerprint liveness detection. IEEE Access 2020;8:183391-400.

Jain AK, Ross AA, Nandakumar K. Introduction to Biometrics. New York, London: Springer; 2011.

Zhang Y, Xiao X, Yang LX, Xiang Y, Zhong SH. Secure and Efficient Outsourcing of PCA-Based Face Recognition. IEEE Trans Inf Forensics Secur 2019;15:1683-95.

Tan X, Deng L, Yang Y, Qu Q, Wen L. Optimized regularized linear discriminant analysis for feature extraction in face recognition. Evol Intell 2019;12:73-82.

Abikoye OC, Shoyemi IF, Aro TO. Comparative analysis of illumination normalizations on principal component analysis based feature extraction for face recognition. FUOYE J Eng Technol 2019;4:67-9.

Agarwal A, Mishra G, Agarwal K. Super resolution technique for face recognition using SVD. Int J Eng Res Technol 2020;8:1-5.

Gao X, Sun Q, Xu H. Multiple-rank supervised canonical correlation analysis for feature extraction, fusion and recognition. IEEE Expert Syst Appl 2017;84:171-85.

Haghighat AM. Low Resolution Face Recognition in Surveillance Systems Using Discriminant Correlation Analysis. Paper Presented at the 12th IEEE International Conference on Automatic Face & Gesture Recognition; 2017.

Zangeneh E, Rahmati M, Mohsenzadeh Y. Low resolution face recognition using a two-branch deep convolutional neural network architecture. Expert Syst Appl 2020;139:1-11.

Wang D, Lu H, Yang MH. Kernel collaborative face recognition. Pattern Recognit 2015;48:3025-37.

Zhao H, Lai ZH, Leung H, Zhang X. Kernel-based nonlinear feature learning. In: Feature Learning and Understanding. Cham: Springer; 2020.

Alam M, Rahman Khan A, Salehin ZU, Uddin M, Jahan Soheli S, Zaman Khan T. Combined PCA-Daugman method: An Ecient technique for face and iris recognition. J Adv Math Comput Sci 2020;35:34-44.

Abiyev RH, Kilic KI. Robust feature extraction and iris recognition for biometric personal identification. InBiometric Systems, Design and Applications 2011. IntechOpen.

Patel RB, Hiran D, Patel J. Biometric Fingerprint Recognition Using Minutiae Score Matching. In: Biometric Fingerprint Recognition Using Minutiae Score Matching. Springer Link; 2020.

Manickam A, Devarasan E, Manogaran G, Kumar Priyan M, Varatharajan R, Hsu CH, et al. Score level based latent fingerprint enhancement and matching using SIFT feature. Multimed Tools Appl 2019;78:3065-85.

Onifade OF, Akinde P, Olubusola Isinkaye F. Circular Gabor wavelet algorithm for fingerprint liveness detection. J Adv Comput Sci Technol 2020;9:1-5.

Jain AK, Nandakumar K, Ross A. 50 years of biometric research: Accomplishments, challenges, and opportunities. IEEE Pattern Recognit Lett 2016;79:80-105.

Saini N, Sinha A. Efficient fusion of face and palmprint in Gabor filtered Wigner domain. Int J Biomet 2020;12:301-16.

Kamlaskar C, Deshmukh S, Gosavi S. Novel canonical correlation analysis based feature level fusion algorithm for multimodal recognition in biometric sensor systems. Sensor Lett 2019;17:75-86.

Tiong LC, Kim ST, Ro YM. Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion. Multimed Tools Appl 2019;78:22743-72.

Haghighat M, Abdel-Mottaleb M, Alhalabi W. Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition. EEE Trans Inf Forensics Secur 2016;11:1984-96.

Zhang H, Li S, Shi Y, Yang J. Graph fusion for finger multimodal biometrics. IEEE Access 2019;7:28607-15.

Kabir W, Omair Ahmad M, Swamy MN. A multi-biometric system based on feature and score level fusions. IEEE Access 2019;7:59437-50.

Kempfert KC, Wang Y, Chen C, Wong SW. A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification. Intelligent Data Analysis. 2020;24:267-90.

Roguia S, Mohamed N. An optimized RBF-neural network for breast cancer classification. Int J Inform Appl Math 2020;1:24-34.

Tang ZH. Leaf image recognition and classification based on GBDT-probabilistic neural network. J Phys Conf Ser 2020;1592:012061.

Prabavathy S, Rathikarani V, Dhanalakshmi P. Classification of musical instruments using SVM and KNN. Int J Innov Technol Explor Eng 2020;9:1186-90.

Hekmatmanesh A, Wu H, Jamaloo F, Li M. A combination of CSP-based method with softmargin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications. Multimed Tools Appl 2020;79:17521-49.

Dan CH, Wei Y, Ravikumar P. Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification. Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119; 2020.

Kusnadi A, Ngadiman VA, Prasetya SG. Image Restoration Effect on DCT High Frequency Removal and Wiener Algorithm for Detecting Facial Key Points. Vol. 7. Proceeding of the Electrical Engineering Computer Science and Informatics; 2020.

Phillips PJ, Newton EM. Meta-Analysis of Face Recognition Algorithms. In: 5th IEEE Conference on Automatic Face and Gesture Recognition, Washington DC; 2002.

Tallón-Ballesteros AJ. Computation of Virtual Training Samples and the Experiments on Face Recognition. Fuzzy Systems and Data Mining V: Proceedings of FSDM 2019. 2019;320:212.

Ihsanto E, Kurniawan J, Husna D, Alfan Presekal A, Ramli K. Development and analysis of a zeta method for low-cost, camera-based iris recognition. Int J Adv Comput Sci Appl 2020;11:320-6.

Bengio S, Mariéthoz J. A statistical significance test for person authentication. InProceedings of Odyssey 2004: The Speaker and Language Recognition Workshop 2004 (No. CONF).


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477